QuanZhenTong

Overview

Quanzhentong is an AI medical assistant built on large language models. Using speech recognition and natural language processing, it automatically converts doctor-patient conversations into structured electronic medical records. It frees doctors from tedious documentation, allowing them to dedicate more time and energy to clinical decision-making and patient communication, thereby enhancing medical quality and compassionate care.

The product supports multimodal information input, including real-time microphone collection and audio file import, automatically extracting key diagnostic information. It features built-in intelligent triage and pre-consultation modules that gather patient condition data before the consultation. The system offers two EMR integration options via API and RPA, seamlessly connecting with existing hospital EMR systems, while strictly enforcing data masking and anonymization to ensure patient privacy.

Quanzhentong is applicable to various settings such as outpatient, inpatient, emergency, and telemedicine. It has been deployed in multiple medical institutions, effectively reducing doctors' average daily documentation time by over 50%. It is not only a productivity tool but also a vital assistant in driving digital transformation in healthcare and improving the doctor-patient relationship.

Core capabilities

Voice-generated medical records

Real-time transcription of doctor-patient conversations to automatically create structured electronic medical records.

Multimodal input

Supports multiple input methods such as microphone and audio files.

Intelligent triage and pre-diagnosis

Collects patient condition information in advance to help doctors quickly understand medical history.

Seamless EMR integration

Connects to existing hospital EMR systems via API or RPA.

Privacy and security protection

Data is desensitized and anonymized, with one-way input only and no patient privacy exposure.

AI-assisted communication

Supports three modes—AI with patient, AI with doctor, and AI listening.

Intelligent summary extraction

Automatically identifies key fields such as chief complaint, present illness history, and diagnosis.

Continuous learning optimization

Continuously fine-tunes the large model on medical data to improve accuracy.

Use cases

Outpatient doctors quickly generate standardized outpatient medical records.

Residents automatically record ward round conversations and write them into progress notes.

Emergency departments transcribe resuscitation processes in real time to assist documentation.

Telemedicine platforms automatically generate consultation summaries.

Operating rooms record key actions and medical orders during surgical procedures.

Primary care clinics automate routine clinical documentation.

Hospitals monitor and improve the quality of doctor-patient communication.

Medical education scenarios use simulated consultation records for analysis.